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The advent of Comparative Genomic Hybridization (CGH) data led to the development of new mathematical models and computational methods to automatically infer chromosomal alterations. In this work we tackle a standard clustering problem exploiting the good representation properties of a novel method based on dictionary learning. The identified dictionary atoms, which show co-occuring shared alterations among samples, can be easily interpreted by domain experts. We compare a state-of-the-art approach with an original method on a breast cancer dataset.